feat(docs): add guide on integration ML system via SDKs (#8029)

Co-authored-by: socar-dini <dini@socar.kr>

docs-website/sidebars.js

@@ -318,6 +318,7 @@ module.exports = {
             "docs/api/tutorials/deprecation",
             "docs/api/tutorials/descriptions",
             "docs/api/tutorials/custom-properties",
+            "docs/api/tutorials/ml",
           ],
         },
         {

docs/api/tutorials/ml.md

@@ -0,0 +1,504 @@
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+# ML System
+
+## Why Would You Integrate ML System with DataHub?
+
+Machine learning systems have become a crucial feature in modern data stacks.
+However, the relationships between the different components of a machine learning system, such as features, models, and feature tables, can be complex.
+Thus, it is essential for these systems to be discoverable to facilitate easy access and utilization by other members of the organization.
+
+For more information on ML entities, please refer to the following docs:
+
+- [MlFeature](/docs/generated/metamodel/entities/mlFeature.md)
+- [MlFeatureTable](/docs/generated/metamodel/entities/mlFeatureTable.md)
+- [MlModel](/docs/generated/metamodel/entities/mlModel.md)
+- [MlModelGroup](/docs/generated/metamodel/entities/mlModelGroup.md)
+
+### Goal Of This Guide
+
+This guide will show you how to
+
+- Create ML entities: MlFeature, MlFeatureTable, MlModel, MlModelGroup
+- Read ML entities: MlFeature, MlFeatureTable, MlModel, MlModelGroup
+- Attach MlFeatureTable or MlModel to MlFeature
+
+## Prerequisites
+
+For this tutorial, you need to deploy DataHub Quickstart and ingest sample data.
+For detailed steps, please refer to [Datahub Quickstart Guide](/docs/quickstart.md).
+
+## Create ML Entities
+
+### Create MlFeature
+
+<Tabs>
+<TabItem value="python" label="Python" default>
+
+```python
+{{ inline /metadata-ingestion/examples/library/create_mlfeature.py show_path_as_comment }}
+```
+
+Note that when creating a feature, you can access a list of data sources using `sources`.
+
+</TabItem>
+</Tabs>
+
+### Create MlFeatureTable
+
+<Tabs>
+<TabItem value="python" label="Python" default>
+
+```python
+{{ inline /metadata-ingestion/examples/library/create_mlfeature_table.py show_path_as_comment }}
+```
+
+Note that when creating a feature table, you can access a list of features using `mlFeatures`.
+
+</TabItem>
+</Tabs>
+
+### Create MlModel
+
+Please note that an MlModel represents the outcome of a single training run for a model, not the collective results of all model runs.
+
+<Tabs>
+<TabItem value="python" label="Python" default>
+
+```python
+{{ inline /metadata-ingestion/examples/library/create_mlmodel.py show_path_as_comment }}
+```
+
+Note that when creating a model, you can access a list of features using `mlFeatures`.
+Additionally, you can access the relationship to model groups with `groups`.
+
+</TabItem>
+</Tabs>
+
+### Create MlModelGroup
+
+Please note that an MlModelGroup serves as a container for all the runs of a single ML model.
+
+<Tabs>
+<TabItem value="python" label="Python" default>
+
+```python
+{{ inline /metadata-ingestion/examples/library/create_mlmodel_group.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+### Expected Outcome of creating entities
+
+You can search the entities in DataHub UI.
+
+![feature-table-created](../../imgs/apis/tutorials/feature-table-created.png)
+
+![model-group-created](../../imgs/apis/tutorials/model-group-created.png)
+
+## Read ML Entities
+
+### Read MLFeature
+
+<Tabs>
+<TabItem value="graphql" label="GraphQL" default>
+
+```json
+query {
+  mlFeature(urn: "urn:li:mlFeature:(test_feature_table_all_feature_dtypes,test_BOOL_LIST_feature)"){
+    name
+    featureNamespace
+    description
+    properties {
+      description
+      dataType
+      version {
+        versionTag
+      }
+    }
+  }
+}
+```
+
+Expected response:
+
+```json
+{
+  "data": {
+    "mlFeature": {
+      "name": "test_BOOL_LIST_feature",
+      "featureNamespace": "test_feature_table_all_feature_dtypes",
+      "description": null,
+      "properties": {
+        "description": null,
+        "dataType": "SEQUENCE",
+        "version": null
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="curl" label="Curl" default>
+
+```json
+curl --location --request POST 'http://localhost:8080/api/graphql' \
+--header 'Authorization: Bearer <my-access-token>' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "query": "{ mlFeature(urn: \"urn:li:mlFeature:(test_feature_table_all_feature_dtypes,test_BOOL_LIST_feature)\") { name featureNamespace description properties { description dataType version { versionTag } } } }"
+}'
+```
+
+Expected response:
+
+```json
+{
+  "data": {
+    "mlFeature": {
+      "name": "test_BOOL_LIST_feature",
+      "featureNamespace": "test_feature_table_all_feature_dtypes",
+      "description": null,
+      "properties": {
+        "description": null,
+        "dataType": "SEQUENCE",
+        "version": null
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="python" label="Python">
+
+```python
+{{ inline /metadata-ingestion/examples/library/read_mlfeature.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+### Read MLFeatureTable
+
+<Tabs>
+<TabItem value="graphql" label="GraphQL" default>
+
+```json
+query {
+  mlFeatureTable(urn: "urn:li:mlFeatureTable:(urn:li:dataPlatform:feast,test_feature_table_all_feature_dtypes)"){
+    name
+    description
+    platform {
+      name
+    }
+    properties {
+      description
+      mlFeatures {
+        name
+      }
+    }
+  }
+}
+```
+
+Expected Response:
+
+```json
+{
+  "data": {
+    "mlFeatureTable": {
+      "name": "test_feature_table_all_feature_dtypes",
+      "description": null,
+      "platform": {
+        "name": "feast"
+      },
+      "properties": {
+        "description": null,
+        "mlFeatures": [
+          {
+            "name": "test_BOOL_LIST_feature"
+          },
+          ...
+          {
+            "name": "test_STRING_feature"
+          }
+        ]
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="curl" label="Curl">
+
+```json
+curl --location --request POST 'http://localhost:8080/api/graphql' \
+--header 'Authorization: Bearer <my-access-token>' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "query": "{ mlFeatureTable(urn: \"urn:li:mlFeatureTable:(urn:li:dataPlatform:feast,test_feature_table_all_feature_dtypes)\") { name description platform { name } properties { description mlFeatures { name } } } }"
+}'
+```
+
+Expected Response:
+
+```json
+{
+  "data": {
+    "mlFeatureTable": {
+      "name": "test_feature_table_all_feature_dtypes",
+      "description": null,
+      "platform": {
+        "name": "feast"
+      },
+      "properties": {
+        "description": null,
+        "mlFeatures": [
+          {
+            "name": "test_BOOL_LIST_feature"
+          },
+          ...
+          {
+            "name": "test_STRING_feature"
+          }
+        ]
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="python" label="Python">
+
+```python
+{{ inline /metadata-ingestion/examples/library/read_mlfeature_table.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+### Read MLModel
+
+<Tabs>
+<TabItem value="graphql" label="GraphQL" default>
+
+```json
+query {
+  mlModel(urn: "urn:li:mlModel:(urn:li:dataPlatform:science,scienceModel,PROD)"){
+    name
+    description
+    properties {
+      description
+      version
+      type
+      mlFeatures
+      groups {
+        urn
+        name
+      }
+    }
+  }
+}
+```
+
+Expected Response:
+
+```json
+{
+  "data": {
+    "mlModel": {
+      "name": "scienceModel",
+      "description": "A sample model for predicting some outcome.",
+      "properties": {
+        "description": "A sample model for predicting some outcome.",
+        "version": null,
+        "type": "Naive Bayes classifier",
+        "mlFeatures": null,
+        "groups": []
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="curl" label="Curl" default>
+
+```json
+curl --location --request POST 'http://localhost:8080/api/graphql' \
+--header 'Authorization: Bearer <my-access-token>' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "query": "{ mlModel(urn: \"urn:li:mlModel:(urn:li:dataPlatform:science,scienceModel,PROD)\") { name description properties { description version type mlFeatures groups { urn name } } } }"
+}'
+```
+
+Expected Response:
+
+```json
+{
+  "data": {
+    "mlModel": {
+      "name": "scienceModel",
+      "description": "A sample model for predicting some outcome.",
+      "properties": {
+        "description": "A sample model for predicting some outcome.",
+        "version": null,
+        "type": "Naive Bayes classifier",
+        "mlFeatures": null,
+        "groups": []
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="python" label="Python">
+
+```python
+{{ inline /metadata-ingestion/examples/library/read_mlmodel.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+### Read MLModelGroup
+
+<Tabs>
+<TabItem value="graphql" label="GraphQL" default>
+
+```json
+query {
+  mlModelGroup(urn: "urn:li:mlModelGroup:(urn:li:dataPlatform:science,my-model-group,PROD)"){
+    name
+    description
+    platform {
+      name
+    }
+    properties {
+      description
+    }
+  }
+}
+```
+
+Expected Response: (Note that this entity does not exist in the sample ingestion and you might want to create this entity first.)
+
+```json
+{
+  "data": {
+    "mlModelGroup": {
+      "name": "my-model-group",
+      "description": "my model group",
+      "platform": {
+        "name": "science"
+      },
+      "properties": {
+        "description": "my model group"
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="curl" label="Curl">
+
+```json
+curl --location --request POST 'http://localhost:8080/api/graphql' \
+--header 'Authorization: Bearer <my-access-token>' \
+--header 'Content-Type: application/json' \
+--data-raw '{
+    "query": "{ mlModelGroup(urn: \"urn:li:mlModelGroup:(urn:li:dataPlatform:science,my-model-group,PROD)\") { name description platform { name } properties { description } } }"
+}'
+```
+
+Expected Response: (Note that this entity does not exist in the sample ingestion and you might want to create this entity first.)
+
+```json
+{
+  "data": {
+    "mlModelGroup": {
+      "name": "my-model-group",
+      "description": "my model group",
+      "platform": {
+        "name": "science"
+      },
+      "properties": {
+        "description": "my model group"
+      }
+    }
+  },
+  "extensions": {}
+}
+```
+
+</TabItem>
+<TabItem value="python" label="Python">
+
+```python
+{{ inline /metadata-ingestion/examples/library/read_mlmodel_group.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+## Add ML Entities
+
+### Add MlFeature to MlFeatureTable
+
+<Tabs>
+<TabItem value="python" label="Python">
+
+```python
+{{ inline /metadata-ingestion/examples/library/add_mlfeature_to_mlfeature_table.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+### Add MlFeature to MLModel
+
+<Tabs>
+<TabItem value="python" label="Python">
+
+```python
+{{ inline /metadata-ingestion/examples/library/add_mlfeature_to_mlmodel.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+### Add MLGroup To MLModel
+
+<Tabs>
+<TabItem value="python" label="Python">
+
+```python
+{{ inline /metadata-ingestion/examples/library/add_mlgroup_to_mlmodel.py show_path_as_comment }}
+```
+
+</TabItem>
+</Tabs>
+
+### Expected Outcome of Adding ML Entities
+
+You can access to `Features` or `Group` Tab of each entity to view the added entities.
+
+![feature-added-to-model](../../imgs/apis/tutorials/feature-added-to-model.png)
+
+![model-group-added-to-model](../../imgs/apis/tutorials/model-group-added-to-model.png)

metadata-ingestion/examples/library/add_mlfeature_to_mlfeature_table.py

@@ -0,0 +1,43 @@
+import datahub.emitter.mce_builder as builder
+import datahub.metadata.schema_classes as models
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+from datahub.metadata.schema_classes import MLFeatureTablePropertiesClass
+
+gms_endpoint = "http://localhost:8080"
+# Create an emitter to DataHub over REST
+emitter = DatahubRestEmitter(gms_server=gms_endpoint, extra_headers={})
+
+feature_table_urn = builder.make_ml_feature_table_urn(
+    feature_table_name="my-feature-table", platform="feast"
+)
+feature_urns = [
+    builder.make_ml_feature_urn(
+        feature_name="my-feature2", feature_table_name="my-feature-table"
+    ),
+]
+
+# This code concatenates the new features with the existing features in the feature table.
+# If you want to replace all existing features with only the new ones, you can comment out this line.
+graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
+feature_table_properties = graph.get_aspect(
+    entity_urn=feature_table_urn, aspect_type=MLFeatureTablePropertiesClass
+)
+if feature_table_properties:
+    current_features = feature_table_properties.mlFeatures
+    print("current_features:", current_features)
+    if current_features:
+        feature_urns += current_features
+
+feature_table_properties = models.MLFeatureTablePropertiesClass(mlFeatures=feature_urns)
+# MCP createion
+metadata_change_proposal = MetadataChangeProposalWrapper(
+    entityType="mlFeatureTable",
+    changeType=models.ChangeTypeClass.UPSERT,
+    entityUrn=feature_table_urn,
+    aspect=feature_table_properties,
+)
+
+# Emit metadata! This is a blocking call
+emitter.emit(metadata_change_proposal)

metadata-ingestion/examples/library/add_mlfeature_to_mlmodel.py

@@ -0,0 +1,44 @@
+import datahub.emitter.mce_builder as builder
+import datahub.metadata.schema_classes as models
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+from datahub.metadata.schema_classes import MLModelPropertiesClass
+
+gms_endpoint = "http://localhost:8080"
+# Create an emitter to DataHub over REST
+emitter = DatahubRestEmitter(gms_server=gms_endpoint, extra_headers={})
+
+model_urn = builder.make_ml_model_urn(
+    model_name="my-test-model", platform="science", env="PROD"
+)
+feature_urns = [
+    builder.make_ml_feature_urn(
+        feature_name="my-feature3", feature_table_name="my-feature-table"
+    ),
+]
+
+# This code concatenates the new features with the existing features in the model
+# If you want to replace all existing features with only the new ones, you can comment out this line.
+graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
+model_properties = graph.get_aspect(
+    entity_urn=model_urn, aspect_type=MLModelPropertiesClass
+)
+if model_properties:
+    current_features = model_properties.mlFeatures
+    print("current_features:", current_features)
+    if current_features:
+        feature_urns += current_features
+
+model_properties = models.MLModelPropertiesClass(mlFeatures=feature_urns)
+
+# MCP creation
+metadata_change_proposal = MetadataChangeProposalWrapper(
+    entityType="mlModel",
+    changeType=models.ChangeTypeClass.UPSERT,
+    entityUrn=model_urn,
+    aspect=model_properties,
+)
+
+# Emit metadata!
+emitter.emit(metadata_change_proposal)

metadata-ingestion/examples/library/add_mlgroup_to_mlmodel.py

@@ -0,0 +1,43 @@
+import datahub.emitter.mce_builder as builder
+import datahub.metadata.schema_classes as models
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+
+gms_endpoint = "http://localhost:8080"
+# Create an emitter to DataHub over REST
+emitter = DatahubRestEmitter(gms_server=gms_endpoint, extra_headers={})
+
+model_group_urns = [
+    builder.make_ml_model_group_urn(
+        group_name="my-model-group", platform="science", env="PROD"
+    )
+]
+model_urn = builder.make_ml_model_urn(
+    model_name="science-model", platform="science", env="PROD"
+)
+
+# This code concatenates the new features with the existing features in the feature table.
+# If you want to replace all existing features with only the new ones, you can comment out this line.
+graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
+
+target_model_properties = graph.get_aspect(
+    entity_urn=model_urn, aspect_type=models.MLModelPropertiesClass
+)
+if target_model_properties:
+    current_model_groups = target_model_properties.groups
+    print("current_model_groups:", current_model_groups)
+    if current_model_groups:
+        model_group_urns += current_model_groups
+
+model_properties = models.MLModelPropertiesClass(groups=model_group_urns)
+# MCP createion
+metadata_change_proposal = MetadataChangeProposalWrapper(
+    entityType="mlModel",
+    changeType=models.ChangeTypeClass.UPSERT,
+    entityUrn=model_urn,
+    aspect=model_properties,
+)
+
+# Emit metadata! This is a blocking call
+emitter.emit(metadata_change_proposal)

metadata-ingestion/examples/library/create_mlfeature.py

@@ -0,0 +1,29 @@
+import datahub.emitter.mce_builder as builder
+import datahub.metadata.schema_classes as models
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+
+# Create an emitter to DataHub over REST
+emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
+
+dataset_urn = builder.make_dataset_urn(
+    name="fct_users_deleted", platform="hive", env="PROD"
+)
+feature_urn = builder.make_ml_feature_urn(
+    feature_table_name="my-feature-table",
+    feature_name="my-feature",
+)
+
+#  Create feature
+metadata_change_proposal = MetadataChangeProposalWrapper(
+    entityType="mlFeature",
+    changeType=models.ChangeTypeClass.UPSERT,
+    entityUrn=feature_urn,
+    aspectName="mlFeatureProperties",
+    aspect=models.MLFeaturePropertiesClass(
+        description="my feature", sources=[dataset_urn], dataType="TEXT"
+    ),
+)
+
+# Emit metadata!
+emitter.emit(metadata_change_proposal)

metadata-ingestion/examples/library/create_mlfeature_table.py

@@ -0,0 +1,33 @@
+import datahub.emitter.mce_builder as builder
+import datahub.metadata.schema_classes as models
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+
+# Create an emitter to DataHub over REST
+emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
+
+feature_table_urn = builder.make_ml_feature_table_urn(
+    feature_table_name="my-feature-table", platform="feast"
+)
+feature_urns = [
+    builder.make_ml_feature_urn(
+        feature_name="my-feature", feature_table_name="my-feature-table"
+    ),
+    builder.make_ml_feature_urn(
+        feature_name="my-feature2", feature_table_name="my-feature-table"
+    ),
+]
+feature_table_properties = models.MLFeatureTablePropertiesClass(
+    description="Test description", mlFeatures=feature_urns
+)
+
+# MCP creation
+metadata_change_proposal = MetadataChangeProposalWrapper(
+    entityType="mlFeatureTable",
+    changeType=models.ChangeTypeClass.UPSERT,
+    entityUrn=feature_table_urn,
+    aspect=feature_table_properties,
+)
+
+# Emit metadata!
+emitter.emit(metadata_change_proposal)

metadata-ingestion/examples/library/create_mlmodel.py

@@ -0,0 +1,38 @@
+import datahub.emitter.mce_builder as builder
+import datahub.metadata.schema_classes as models
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+
+# Create an emitter to DataHub over REST
+emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
+model_urn = builder.make_ml_model_urn(
+    model_name="my-test-model", platform="science", env="PROD"
+)
+model_group_urns = [
+    builder.make_ml_model_group_urn(
+        group_name="my-model-group", platform="science", env="PROD"
+    )
+]
+feature_urns = [
+    builder.make_ml_feature_urn(
+        feature_name="my-feature", feature_table_name="my-feature-table"
+    ),
+    builder.make_ml_feature_urn(
+        feature_name="my-feature2", feature_table_name="my-feature-table"
+    ),
+]
+
+metadata_change_proposal = MetadataChangeProposalWrapper(
+    entityType="mlModel",
+    changeType=models.ChangeTypeClass.UPSERT,
+    entityUrn=model_urn,
+    aspectName="mlModelProperties",
+    aspect=models.MLModelPropertiesClass(
+        description="my feature",
+        groups=model_group_urns,
+        mlFeatures=feature_urns,
+    ),
+)
+
+# Emit metadata!
+emitter.emit(metadata_change_proposal)

metadata-ingestion/examples/library/create_mlmodel_group.py

@@ -0,0 +1,25 @@
+import datahub.emitter.mce_builder as builder
+import datahub.metadata.schema_classes as models
+from datahub.emitter.mcp import MetadataChangeProposalWrapper
+from datahub.emitter.rest_emitter import DatahubRestEmitter
+
+# Create an emitter to DataHub over REST
+emitter = DatahubRestEmitter(gms_server="http://localhost:8080", extra_headers={})
+model_group_urn = builder.make_ml_model_group_urn(
+    group_name="my-model-group", platform="science", env="PROD"
+)
+
+
+metadata_change_proposal = MetadataChangeProposalWrapper(
+    entityType="mlModelGroup",
+    changeType=models.ChangeTypeClass.UPSERT,
+    entityUrn=model_group_urn,
+    aspectName="mlModelGroupProperties",
+    aspect=models.MLModelGroupPropertiesClass(
+        description="my model group",
+    ),
+)
+
+
+# Emit metadata!
+emitter.emit(metadata_change_proposal)

metadata-ingestion/examples/library/read_mlfeature.py

@@ -0,0 +1,13 @@
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+
+# Imports for metadata model classes
+from datahub.metadata.schema_classes import MLFeaturePropertiesClass
+
+# First we get the current owners
+gms_endpoint = "http://localhost:8080"
+graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
+
+urn = "urn:li:mlFeature:(test_feature_table_all_feature_dtypes,test_BOOL_feature)"
+result = graph.get_aspect(entity_urn=urn, aspect_type=MLFeaturePropertiesClass)
+
+print(result)

metadata-ingestion/examples/library/read_mlfeature_table.py

@@ -0,0 +1,13 @@
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+
+# Imports for metadata model classes
+from datahub.metadata.schema_classes import MLFeatureTablePropertiesClass
+
+# First we get the current owners
+gms_endpoint = "http://localhost:8080"
+graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
+
+urn = "urn:li:mlFeatureTable:(urn:li:dataPlatform:feast,test_feature_table_all_feature_dtypes)"
+result = graph.get_aspect(entity_urn=urn, aspect_type=MLFeatureTablePropertiesClass)
+
+print(result)

metadata-ingestion/examples/library/read_mlmodel.py

@@ -0,0 +1,13 @@
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+
+# Imports for metadata model classes
+from datahub.metadata.schema_classes import MLModelPropertiesClass
+
+# First we get the current owners
+gms_endpoint = "http://localhost:8080"
+graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
+
+urn = "urn:li:mlModel:(urn:li:dataPlatform:science,scienceModel,PROD)"
+result = graph.get_aspect(entity_urn=urn, aspect_type=MLModelPropertiesClass)
+
+print(result)

metadata-ingestion/examples/library/read_mlmodel_group.py

@@ -0,0 +1,13 @@
+from datahub.ingestion.graph.client import DatahubClientConfig, DataHubGraph
+
+# Imports for metadata model classes
+from datahub.metadata.schema_classes import MLModelGroupPropertiesClass
+
+# First we get the current owners
+gms_endpoint = "http://localhost:8080"
+graph = DataHubGraph(DatahubClientConfig(server=gms_endpoint))
+
+urn = "urn:li:mlModelGroup:(urn:li:dataPlatform:science,my-model-group,PROD)"
+result = graph.get_aspect(entity_urn=urn, aspect_type=MLModelGroupPropertiesClass)
+
+print(result)